Single visits to active wolf dens do not impact wolf pup recruitment or pack size
Bibliographic record
Abstract
Evaluating methods used to capture and mark neonates is necessary for ensuring research methods are ethical, follow best practices, and do not have long‐term unintended impacts on neonates or populations. We used a quasi‐experimental approach (reference versus treatment) to determine whether visiting wolf dens and marking wolf Canis lupus pups affects important wolf population metrics. Specifically, we examined whether pup recruitment and pack size differed between packs where we visited dens and handled pups (‘disturbed packs' = treatment group) and those where we did not visit dens (‘undisturbed packs' = reference group). During 2019–2023, we studied 43 wolf packs and litters, 19 of which were disturbed packs and 24 of which were undisturbed. We found no difference in recruitment or pack size between disturbed and undisturbed wolf packs. However, we did observe substantial annual variation in recruitment and pack size, which indicated that other ecological factors (e.g. prey abundance) were likely responsible for annual changes in recruitment and pack size. Our findings are consistent with several other studies, and together this research indicates that wolf dens can be visited once and wolf pups handled briefly for research purposes without having a measurable effect on recruitment and pack size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".